JNR Deforestation Risk Maps
Abstract
A workflow for deriving deforestation risk maps in accordance with Verra’s new methodology for unplanned deforestation allocation in jurisdictional nested REDD+ projects using VT0007 toolset.
Keywords
REDD+, VCS, Verra, Carbon verification, Jurisdictional
Summary
The following details a possible workflow approach to Verra’s recommended sequence of deforestation risk map development Verra (2021).
Training data was sourced from a filtered subset of the global training sample data developed by(Stanimirova et al. 2023). Satellite imagery was sourced from the Landsat Collection-2 Tier-1 Level-2 raster dataset. Data acquisition and pre-processing of satellite imagery was implemented in a google colab runtime here.
1. Workflow in R -> sits
Environment setup
All required R packages are listed alphabetically and installed together via the hidden setup chunk at the top of this R-markdown file. All packages and kernel versions are also listed using the session_info() command at the bottom of the markdown.
Clone github repository
To copy and run these scripts locally, you may clone the project’s github repository to your machine using git commands from any terminal (git installation here) or by opening a ‘new project’ with ‘version control’ settings from the File menu options in your IDE. To assign correct ‘version control’, enter the repository’s github URL, which can be located here (Figure 2). Cloning will download all input, output, and script files and subfolders to your chosen directory, which you can then open, run and edit locally without github syncing or with to contribute suggested commits to certain branches as you please.
Restore virtual environment
To avoid issues with IDE settings and dotfiles, run the following code chunk of virtual environment setup from a terminal that is external to RStudio or VScode. Open the terminal in the top folder of the cloned directory and run the following. To update a previously loaded environment, simply run pip3 install -r requirements.txt and skip the following.
# create virtual environment
python3 -m venv working_director_name
# activate environment's python
source working_director_name/bin/activate
# check python activation
python3
import sys
print(sys.executable)
quit()
# restore environment of cloned repo
python3 pip install -r requirements.txt
# install packages manually
python3 -m pip install numpy jupyter earthengine-api
# save added packages for later use
python3 -m pip freeze > requirements.txtAssign rgee kernel, gcs directory & credentials
If running this script and configuring your environment for the first time, run the code chunk directly below. If updating a previously loaded environment, only run the second code chunk below.
# assign reticulate to the python located in the project's virtual environment
reticulate::use_python("./bin/python3")
reticulate::py_run_string("import ee; ee.Initialize()")
# assign rgee to the same python in the virtual environment & restart (Windows restart required)
rgee::ee_install_set_pyenv(py_path = "./bin/python3", py_env = "./", confirm = F)
# save earth engine username and password to plain text file & assign location:
rgee::ee_path = path.expand("~/.config/earthengine/seamusrobertmurphy/credentials", confirm = F)
# install earth engine api via the rgee package
rgee::ee_install()
# activate and authenticate yours and the project's google earth engine access
rgee::ee_Authenticate()
rgee::ee_Initialize(user = "username_here", gcs = T, drive = T)
# look for any prompts in active terminal window below o
# --- advanced system configuration optional ---
# save a Service Account Key to enable 'sign-in' & web renders w/ these links:
# SaK setup: https://cloud.google.com/iam/docs/service-accounts
# SaK guide: https://r-spatial.github.io/rgee/articles/rgee05.html
# point to your saved SaK credientials & assign users
SaK_file = "/home/seamus/Repos/api-keys/SaK_rgee.json"
ee_utils_sak_copy(sakfile = SaK_file, users = "seamusrobertmurphy")
# confirm project_id & bucket
project_id <- ee_get_earthengine_path() %>%
list.files(., "\\.json$", full.names = TRUE) %>%
jsonlite::read_json() %>%
'$'(project_id)
# create a google cloud bucket for storing project outputs
googleCloudStorageR::gcs_create_bucket("deforisk_bucket_1", projectId = project_id)
# validate SaK credentials
ee_utils_sak_validate(
sakfile = SaK_file,
bucket = "deforisk_bucket_1",
quiet = F
)# assign reticulate to the python located in the project's virtual environment
reticulate::use_python("./bin/python3")
reticulate::py_run_string("import ee; ee.Initialize()")
# assign rgee to the same python in the virtual environment & restart (Windows restart required)
rgee::ee_install_set_pyenv(py_path = "./bin/python3", py_env = "./", confirm = F)
rgee::ee_path = path.expand("/home/seamus/.config/earthengine/seamusrobertmurphy/credentials", confirm = F)
# activate and authenticate yours and the project's google earth engine access
rgee::ee_Initialize(user = "seamusrobertmurphy", gcs = T, drive = T)Jurisdictional boundaries
# assign master crs
crs_master = sf::st_crs("epsg:4326")
# derive aoi windows
aoi_country = geodata::gadm(country="GUY", level=0, path=tempdir()) |>
sf::st_as_sf() |> sf::st_cast() |> sf::st_transform(crs_master)
aoi_states = geodata::gadm(country="GUY", level=1, path=tempdir()) |>
sf::st_as_sf() |> sf::st_cast() |> sf::st_transform(crs_master) |>
dplyr::rename(State = NAME_1)
aoi_target = dplyr::filter(aoi_states, State == "Barima-Waini")
aoi_target_ee = rgee::sf_as_ee(aoi_target)Error : could not find a Python environment for /Error: Installation of Python not found, Python bindings not loaded.
See the Python "Order of Discovery" here: https://rstudio.github.io/reticulate/articles/versions.html#order-of-discovery.# visualize
tmap::tmap_mode("view")
tmap::tm_shape(aoi_states) + tmap::tm_borders(col = "white", lwd = 0.5) +
tmap::tm_text("State", col = "white", size = 1, alpha = 0.3, just = "bottom") +
tmap::tm_shape(aoi_country) + tmap::tm_borders(col = "white", lwd = 1) +
tmap::tm_shape(aoi_target) + tmap::tm_borders(col = "red", lwd = 2) +
tmap::tm_text("State", col = "red", size = 2) +
tmap::tm_basemap("Esri.WorldImagery")Satellite data acquisition
We assembled and processed a data cube for the ten year historical reference period (HRP) between start date 2014-01-01 and end date 2024-12-31 for the state of Barina Waini, Guyana. Masking is applied to cloud, shadow and water surfaces with median normalization using a cloudless pixel ranking.
cube_2014 = sits_cube(
source = "MPC",
collection = "LANDSAT-C2-L2",
data_dir = here::here("cubes", "mosaic"),
bands = c("BLUE", "GREEN", "RED", "RED", "NIR08", "SWIR16", "SWIR22", "NDVI"),
version = "mosaic"
)
sits_view(cube_2014, band = "NDVI", date = "2014-01-11", opacity = 1)tmap::tmap_options(max.raster = c(plot = 80000000, view = 100000000))
#rgb_2014 = raster::raster("./cubes/mosaic/LANDSAT_TM-ETM-OLI_231055_RGB_2014-01-11.tif")
rgb_2014 = terra::rast("./cubes/mosaic/LANDSAT_TM-ETM-OLI_231055_RGB_2014-01-11.tif")
rgb_2014 = raster::stretch(rgb_2014, minv = 0, maxv = 255, minq = 0.1, maxq = 0.99)
tmap::tm_shape(rgb_2014) +
tmap::tm_rgb()
B1 = raster::raster("LC08_L2SP_166072_20210819_20210827_02_T1_SR_B1.TIF")
B2 = raster::raster("LC08_L2SP_166072_20210819_20210827_02_T1_SR_B2.TIF")
B3 = raster::raster("LC08_L2SP_166072_20210819_20210827_02_T1_SR_B3.TIF")
B4 = raster::raster("LC08_L2SP_166072_20210819_20210827_02_T1_SR_B4.TIF")
B5 = raster::raster("LC08_L2SP_166072_20210819_20210827_02_T1_SR_B5.TIF")
B6 = raster::raster("LC08_L2SP_166072_20210819_20210827_02_T1_SR_B6.TIF")
B7 = raster::raster("LC08_L2SP_166072_20210819_20210827_02_T1_SR_B7.TIF")
stack_2014 <- stack(B5, B4, B3)Error: [rast] file does not exist: ./cubes/mosaic/LANDSAT_TM-ETM-OLI_231055_RGB_2014-01-11.tifError in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'stretch': object 'rgb_2014' not foundError in FUN(X[[i]], ...): object 'rgb_2014' not foundError in .rasterObjectFromFile(x, band = band, objecttype = "RasterLayer", : Cannot create a RasterLayer object from this file. (file does not exist)Error in .rasterObjectFromFile(x, band = band, objecttype = "RasterLayer", : Cannot create a RasterLayer object from this file. (file does not exist)Error in .rasterObjectFromFile(x, band = band, objecttype = "RasterLayer", : Cannot create a RasterLayer object from this file. (file does not exist)Error in .rasterObjectFromFile(x, band = band, objecttype = "RasterLayer", : Cannot create a RasterLayer object from this file. (file does not exist)Error in .rasterObjectFromFile(x, band = band, objecttype = "RasterLayer", : Cannot create a RasterLayer object from this file. (file does not exist)Error in .rasterObjectFromFile(x, band = band, objecttype = "RasterLayer", : Cannot create a RasterLayer object from this file. (file does not exist)Error in .rasterObjectFromFile(x, band = band, objecttype = "RasterLayer", : Cannot create a RasterLayer object from this file. (file does not exist)Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'stack': object 'B5' not foundLULC classification
We extracted a training sample from the GLanCE dataset of annual times series points that intersect with our spatial of temporal window of interest(Woodcock et al., n.d.)). These training samples include locations of 7 land cover classes (Figure 2) which were
Training samples are fitted to a Random Forest model and post-processed with a Bayesian smoothing and then evaluated using confusion matrix.
The classifier is then calibrated by mapping pixel uncertainty, adding new samples in areas of high uncertainty, reclassifying with improved samples and re-evaluated using confusion matrix.
# extract dataset from ee: https://gee-community-catalog.org/projects/glance_training/?h=training
#glance_training_url = "https://drive.google.com/file/d/1FhWTpSGFRTodDCY2gSGhssLuP2Plq4ZE/view?usp=drive_link"
# file_name = "glance_training.csv"
# download.file(url = url, path = here::here("training"), destfile = file_name)
glance_training = read.csv(here::here("training", "glance_training.csv"))
glimpse(glance_training)
glance_training_edit = dplyr::select(
glance_training, Lon, Lat, Glance_Class_ID_level1, Start_Year, End_Year) |>
dplyr::rename(longitude = Lon) |>
dplyr::rename(latitude = Lat) |>
dplyr::rename(label = Glance_Class_ID_level1) |>
dplyr::select() |>
mutate(start_date = ymd(paste(Start_Year, "01", "01", sep = "-"))) |>
mutate(end_date = ymd(paste(End_Year, "01", "01", sep = "-"))) |>
dplyr::select(-Start_Year, -End_Year)
glimpse(glance_training_edit)
# convert to sf for spatial filtering
glance_training_sf = sf::st_as_sf(
glance_training_edit, coords = c("longitude", "latitude"))
tmap::tm_shape(glance_training_sf) +
tm_dots(col = "red", size = 0.1, alpha = 0.7) # Points in red
# Plot the map
tmap_mode("view") # Interactive map
tm_map
tmap::tmap_mode("view")
tmap::tm_shape(glance_training_sf) + tmap::tm_borders(col = "white", lwd = 0.5) +
tmap::tm_text("State", col = "white", size = 1, alpha = 0.3, just = "bottom") +
tmap::tm_shape(aoi_country) + tmap::tm_borders(col = "white", lwd = 1) +
tmap::tm_shape(aoi_target) + tmap::tm_borders(col = "red", lwd = 2) +
tmap::tm_text("State", col = "red", size = 2) +
tmap::tm_basemap("Esri.WorldImagery")
glance_training_sf = sf::st_intersection(glance_training_sf, aoi_target)
plot(st_geometry(glance_training_sf))
#dplyr::filter(start_date=="2014-01-01" | end_date=="2014-01-01" | start_date=="2024-01-01" | end_date=="2024-01-01")
glimpse(glance_training_edit)
labels <- c(
"1" = "Water",
"2" = "Ice",
"3" = "Urban",
"4" = "Barren",
"5" = "Trees",
"6" = "Shrublands",
"7" = "Herbaceous"
)
data("samples_prodes_4classes")
# Select the same three bands used in the data cube
samples_4classes_3bands <- sits_select(
data = samples_prodes_4classes,
bands = c("B02", "B8A", "B11")
)
# Train a random forest model
rfor_model <- sits_train(
samples = samples_4classes_3bands,
ml_method = sits_rfor()
)
# Classify the small area cube
s2_cube_probs <- sits_classify(
data = s2_reg_cube_ro,
ml_model = rfor_model,
output_dir = "./cubes/02_class/",
memsize = 15,
multicores = 5
)
# Post-process the probability cube
s2_cube_bayes <- sits_smooth(
cube = s2_cube_probs,
output_dir = "./cubes/02_class/",
memsize = 16,
multicores = 4
)
# Label the post-processed probability cube
s2_cube_label <- sits_label_classification(
cube = s2_cube_bayes,
output_dir = "./cubes/02_class/",
memsize = 16,
multicores = 4
)
plot(s2_cube_label)Map uncertainty
To improve model performance, we estimate class uncertainty and plot these pixel error metrics. Results below reveal highest uncertainty levels in classification of wetland and water areas.
# Calculate the uncertainty cube
s2_cube_uncert <- sits_uncertainty(
cube = s2_cube_bayes,
type = "margin",
output_dir = "./cubes/03_error/",
memsize = 16,
multicores = 4
)
plot(s2_cube_uncert)As expected, the places of highest uncertainty are those covered by surface water or associated with wetlands. These places are likely to be misclassified. For this reason, sits provides sits_uncertainty_sampling(), which takes the uncertainty cube as its input and produces a tibble with locations in WGS84 with high uncertainty (Camara et al., n.d.).
# Find samples with high uncertainty
new_samples <- sits_uncertainty_sampling(
uncert_cube = s2_cube_uncert,
n = 20,
min_uncert = 0.5,
sampling_window = 10
)
# View the location of the samples
sits_view(new_samples)Add training samples
We can then use these points of high-uncertainty as new samples to add to our current training dataset. Once we identify their feature classes and relabel them correctly, we append them to derive an augmented samples_round_2.
# Label the new samples
new_samples$label <- "Wetland"
# Obtain the time series from the regularized cube
new_samples_ts <- sits_get_data(
cube = s2_reg_cube_ro,
samples = new_samples
)
# Add new class to original samples
samples_round_2 <- dplyr::bind_rows(
samples_4classes_3bands,
new_samples_ts
)
# Train a RF model with the new sample set
rfor_model_v2 <- sits_train(
samples = samples_round_2,
ml_method = sits_rfor()
)
# Classify the small area cube
s2_cube_probs_v2 <- sits_classify(
data = s2_reg_cube_ro,
ml_model = rfor_model_v2,
output_dir = "./cubes/02_class/",
version = "v2",
memsize = 16,
multicores = 4
)
# Post-process the probability cube
s2_cube_bayes_v2 <- sits_smooth(
cube = s2_cube_probs_v2,
output_dir = "./cubes/04_smooth/",
version = "v2",
memsize = 16,
multicores = 4
)
# Label the post-processed probability cube
s2_cube_label_v2 <- sits_label_classification(
cube = s2_cube_bayes_v2,
output_dir = "./cubes/05_tuned/",
version = "v2",
memsize = 16,
multicores = 4
)
# Plot the second version of the classified cube
plot(s2_cube_label_v2)Remap uncertainty
# Calculate the uncertainty cube
s2_cube_uncert_v2 <- sits_uncertainty(
cube = s2_cube_bayes_v2,
type = "margin",
output_dir = "./cubes/03_error/",
version = "v2",
memsize = 16,
multicores = 4
)
plot(s2_cube_uncert_v2)Accuracy assessment
To select a validation subset of the map, sits recommends Cochran’s method for stratified random sampling (Cochran 1977). The method divides the population into homogeneous subgroups, or strata, and then applying random sampling within each stratum. Alternatively, ad-hoc parameterization is suggested as follows.
ro_sampling_design <- sits_sampling_design(
cube = s2_cube_label_v2,
expected_ua = c(
"Burned_Area" = 0.75,
"Cleared_Area" = 0.70,
"Forest" = 0.75,
"Highly_Degraded" = 0.70,
"Wetland" = 0.70
),
alloc_options = c(120, 100),
std_err = 0.01,
rare_class_prop = 0.1
)
# show sampling desing
ro_sampling_designSplit train/test data
ro_samples_sf <- sits_stratified_sampling(
cube = s2_cube_label_v2,
sampling_design = ro_sampling_design,
alloc = "alloc_120",
multicores = 4,
shp_file = "./samples/ro_samples.shp"
)
sf::st_write(ro_samples_sf,
"./samples/ro_samples.csv",
layer_options = "GEOMETRY=AS_XY",
append = FALSE # TRUE if editing existing sample
)Confusion matrix
# Calculate accuracy according to Olofsson's method
area_acc <- sits_accuracy(s2_cube_label_v2,
validation = ro_samples_sf,
multicores = 4
)
# Print the area estimated accuracy
area_acc
# Print the confusion matrix
area_acc$error_matrixTimes series visualization
summary(as.data.frame(ro_samples_sf))Deforestation binary map
Deforestation risk map
2. Workflow in Python -> GEE
# Set your Python ENV
Sys.setenv("RETICULATE_PYTHON" = "/usr/bin/python3")
# Set Google Cloud SDK
Sys.setenv("EARTHENGINE_GCLOUD" = "~/seamus/google-cloud-sdk/bin/")
library(rgee)
ee_Authenticate()
ee_install_upgrade()
ee_Initialize()Housekeeping
# convert markdown to script.R
knitr::purl("VT0007-deforestation-risk-map.qmd")
# display environment setup
devtools::session_info()Environment snapshot
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.3.0 (2023-04-21)
os macOS 15.6.1
system aarch64, darwin20
ui X11
language (EN)
collate en_CA.UTF-8
ctype en_CA.UTF-8
tz America/Vancouver
date 2025-08-26
pandoc 3.7.0.2 @ /opt/local/bin/ (via rmarkdown)
quarto 1.7.33 @ /usr/local/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
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[1] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library
* ── Packages attached to the search path.
──────────────────────────────────────────────────────────────────────────────References
Camara, Gilberto, Rolf Simoes, Felipe Souza, Felipe Menino, Charlotte Pelletier, Pedro R. Andrade, Karine Ferreira, and Gilberto Queiroz. n.d. Uncertainty and Active Learning | Sits: Satellite Image Time Series Analysis on Earth Observation Data Cubes. https://e-sensing.github.io/sitsbook/uncertainty-and-active-learning.html#uncertainty-and-active-learning.
Cochran, William G. 1977. “Sampling Techniques.” Johan Wiley & Sons Inc.
Stanimirova, Radost, Katelyn Tarrio, Konrad Turlej, Kristina McAvoy, Sophia Stonebrook, Kai-Ting Hu, Paulo Arévalo, et al. 2023. “A Global Land Cover Training Dataset from 1984 to 2020.” Scientific Data 10 (1): 879. https://doi.org/10.1038/s41597-023-02798-5.
Verra. 2021. “VT0007 Unplanned Deforestation Allocation Tool.”
Woodcock, Curtis, Pontus Olofsson, Thomas Loveland, Chris Barber, and Zhe Zhu. n.d. “Global Land Cover Estimation (GLanCE) Product User Guide Version 1.0 August 2022.”